Randeng-TransformerXL-5B-Abduction-Chinese

简介 Brief Introduction

基于Transformer-XL的中文反绎（溯因）推理生成模型。

Chinese abductive reasoning model based on Transformer-XL.

模型分类 Model Taxonomy

需求 Demand 任务 Task 系列 Series 模型 Model 参数 Parameter 额外 Extra
通用 General 自然语言生成 NLG 燃灯 Randeng TransformerXL 5.0B 中文-因果推理 Chinese-Reasoning

模型信息 Model Information

数据准备 Corpus Preparation

  • 悟道语料库（280G版本）

  • 因果语料库（2.3M个样本）：基于悟道语料库（280G版本），通过关联词匹配、人工标注 + GTSFactory筛选、数据清洗等步骤获取的具有因果关系的句子对

  • Wudao Corpus (with 280G samples)

  • Wudao Causal Corpus (with 2.3 million samples): Based on the Wudao corpus (280G version), sentence pairs with causality were obtained through logic indicator matching, manual annotation + GTSFactory, and data cleaning.

训练流程 Model Training

  1. 在悟道语料库（280G版本）上进行预训练

  2. 在1.5M因果语料上进行反绎生成任务的训练

  3. 基于其余0.8M因果语料，协同Randeng-TransformerXL-5B-Deduction-Chinese和Erlangshen-Roberta-330M-Causal-Chinese进行Self-consistent闭环迭代训练

    • 两个生成模型基于核采样和贪心的方式进行因果推理和反绎推理，产生大量伪样本；

    • Erlangshen-Roberta-330M-Causal-Chinese模型对伪样本句子对的因果关系进行打分，筛选供自身以及生成模型训练的样本

First, the Transformer-XL model was pre-trained on the Wudao Corpus (with 280G samples) and annotated similar-sentence pair dataset (same as Randeng-TransformerXL-1.1B-Paraphrasing-Chinese). Then, the model was trained on our causal corpus (about 1.5 million samples) for the abductive reasoning task. At last, based on the remaining 0.8 million samples of the causal corpus, we conducted self-consistent learning on this model, cooperating with Randeng-TransformerXL-5B-Deduction-Chinese and Erlangshen-Roberta-330M-Causal-Chinese. Specifically, two generative models performed deductive reasoning and abductive reasoning based on each sample respectively, generating a large number of pseudo-samples; Erlangshen-Roberta-330M-Causal-Chinese scored the causality of the pseudo-samples and selected the training data for itself and the generative models in the next iteration.

加载模型 Loading Models

git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
cd Fengshenbang-LM

from fengshen.models.transfo_xl_reasoning import TransfoXLModel
from transformers import T5Tokenizer as TransfoXLTokenizer

model = TransfoXLModel.from_pretrained('IDEA-CCNL/Randeng-TransformerXL-5B-Abduction-Chinese')
tokenizer = TransfoXLTokenizer.from_pretrained(
    "IDEA-CCNL/Randeng-TransformerXL-5B-Abduction-Chinese",
    eos_token='<|endoftext|>',
    pad_token='<|endoftext|>',
    extra_ids=0
)
tokenizer.add_special_tokens({'bos_token': '<bos>'})

使用示例 Usage Example

from fengshen.models.transfo_xl_reasoning import abduction_generate

input_text = "玉米价格持续上涨"
input_texts = ["玉米价格持续上涨", "玉米价格持续上涨"]
print(abduction_generate(model, tokenizer, input_text, device=0))
print(abduction_generate(model, tokenizer, input_texts, device=0))

一些例子 Some Cases

（以下内容为模型生成，不代表本单位观点）

  1. 玉米价格持续上涨

    • 玉米库存较低，需求增加

    • 东北地区受降雨天气影响，玉米生长受到影响

    • 今年玉米种植面积大幅度下降

  2. 玉米价格下跌

    • 玉米的库存量大，需求量低

    • 今年玉米产量创新高，而需求不足

    • 目前玉米市场处于供大于求的状态，再加上近期华北地区遭遇了强降雨天气，玉米质量下降

  3. 农作物大量死亡

    • 旱灾持续时间长，又无雨，土壤干裂，作物得不到水分

    • 霜冻来临，气温骤降，植物受冻

    • 许多农民为了使农作物能够长得更好，使用更多的农药，并且没有合理的休耕措施

  4. 鲸鱼需要消耗大量的能量

    • 鲸鱼的体型庞大，新陈代谢速度又快

    • 鲸鱼的身体结构特殊，需要消耗大量的能量来维持身体结构的稳定

  5. 实体经济融资难、融资贵

    • 融资渠道单一，实体经济难以获得充足的资金

    • 实体经济融资主要依赖抵押、担保、信贷等间接融资方式，存在抵押物不足、担保机制不完善等问题

    • 实体经济往往需要大量的资金，而银行受制于风险控制、资本充足率等要求，很难大量发放贷款

  6. 火山爆发导致植物死亡

    • 火山灰会阻碍植物吸收阳光

    • 火山灰的飘散，导致植物无法吸收到足够的氧气

    • 火山喷发时，岩浆温度极高，植物无法承受

引用 Citation

如果您在您的工作中使用了我们的模型，可以引用我们的论文：

If you are using the resource for your work, please cite the our paper:

@article{fengshenbang,
  author    = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang},
  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
  journal   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}
}

也可以引用我们的网站:

You can also cite our website:

@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}